Modeling the relative impact of capsular tissue effects on implanted glucose sensor time lag and signal attenuation

• Published in: Analytical and bioanalytical chemistry Vol. 398; no. 4; pp. 1695 - 1705
• Main Authors: Novak, Matthew T, Yuan, Fan, Reichert, William M
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.10.2010; Springer-Verlag; Springer
• Subjects: Aldehydes; Analysis; Analytical Chemistry; Animals; Attenuation; Biochemistry; Biological products; Biological Transport; Biomaterials; Biosensing Techniques - instrumentation; Biosensing Techniques - methods; biosensors; Blood; Blood sugar; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Density; Diffusion; Food Science; Glucose; Glucose - chemistry; Glucose - pharmacokinetics; Glucose metabolism; Kinetics; Laboratory Medicine; Mathematical analysis; Mathematical models; Microvessels - metabolism; Models, Biological; Monitoring/Environmental Analysis; Original Paper; Porosity; Prostheses and Implants; Rats; Sensors; Time lag; Biosensors; Modeling; Biomaterials
• ISSN: 1618-2642; 1618-2650
• DOI: 10.1007/s00216-010-4097-6

Little is known mechanistically about why implanted glucose sensors lag behind blood glucose levels in both the time to peak sensor response and the magnitude of peak sensor response. A mathematical model of glucose transport from capillaries through surrounding tissue to the sensor surface was constructed to address how different aspects of the tissue affect glucose transport to an implanted sensor. Physiologically relevant values of capsule diffusion coefficient, capsule porosity, cellular glucose consumption, capsule thickness, and subcutaneous vessel density were used as inputs to create simulated sensor traces that mimic experimental instances of time lag and concentration attenuation relative to a given blood glucose profile. Using logarithmic sensitivity analysis, each parameter was analyzed to study the effect of these variables on both lag and attenuation. Results identify capsule thickness as the strongest determinant of sensor time lag, while subcutaneous vessel density and capsule porosity had the largest effects on attenuation of glucose that reaches the sensor surface. These findings provide mechanistic insight for the rational design of sensor modifications that may alleviate the deleterious consequences of tissue effects on implanted sensor performance.